Muhammad Shafique

Muhammad Shafique
Univ.Prof. Dr.-Ing.
Phone +43 (1) 58801-18252
Fax +43 1 5880118297

Vienna University of Technology
Institute of Computer Engineering
Embedded Computing Systems
Treitlstraße 3, 1040 Wien, Österreich



I am leading several research projects in the area of embedded machine learning with a focus on energy-efficiency, reliability, and security, energy-efficient computing for smart CPS and IoT systems, low-power design, hardware security, and approximate computing, covering various abstraction levels of the system stack. In all these topics, I am exploring both basic and applied research related challenges, covering analysis, modeling, design & optimization, as well as validation & verification.

My research at TU Wien has been funded by FWF, DFG, FFG, Intel, TU Wien, BMWFW, and EU.

Embedded Machine Learning

The goal of this project is to investigate novel architectures and design tools for energy-efficient ML inference and training. The inference accelerators would be deployed at the IoT-Edge, while the training accelerators would be deployed at the Cloud.

  • Energy-efficient architectures for ML inference and training
  • Hardware/software co-design for embedded Deep Learning
  • Specialized memory architectures for ML
  • Neuromorphic architectures
  • ReRAM-based designs
  • Optimizations for Spiking Neural Networks (SNNs)

Robust Machine Learning

  • Dependability for DNNs and SNNs
  • Security for DNNs and SNNs: attacks and defenses
  • Formal Verification for ML
  • Applications in Autonomous Driving, Robotics, and Healthcare

Cross-Layer Dependability for Smart Computing Systems

  • Cross-layer reliability analysis, modeling, and optimizations
  • Multi-objective optimizations for multiple fault types (soft-errors, aging, variability, etc.)
  • Self-healing and self-recovery
  • Reliability for emerging technologies
  • Machine learning based reliability analysis and optimization

Hardware Security

  • Hardware-assisted security
  • Hardware Trojans
  • FPGA Security in the Cloud
  • Online anomaly detection
  • Machine Learning based Cross-Layer Security
  • Hardware security for Car-2-Car communication in autonomous driving scenarios

IoT-Healthcare with Intelligent Wearables


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Approximate Computing


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Energy-Efficient Code Generation for IoT-Edge Devices


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